R version 4.6.0 beta (2026-04-12 r89874 ucrt) -- "Because it was There"
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> 
> library("mvtnorm")
> 
> set.seed(29)
> 
> chk <- function(...) stopifnot(isTRUE(all.equal(..., check.attributes = FALSE)))
> 
> ### N samples with N different covariance matrices
> 
> N <- 10
> J <- 5
> prm <- runif(N * J * (J + 1) / 2) + 1
> m <- matrix(rnorm(N * J), nrow = J)
> Z <- matrix(rnorm(N * J), ncol = N)
> W <- matrix(runif((J - 1) * 100), nrow = J - 1)
> 
> thischeck <- expression({
+   lt <- ltMatrices(matrix(prm[1: (N * J * (J + c(-1, 1)[dg + 1L]) / 2)], ncol = N), 
+                    diag = dg)
+   lt <- ltMatrices(lt, diag = dg, byrow = br)
+   d <- Mult(lt, m)
+   Y <- solve(lt, Z) + m
+   ll1 <- sum(dnorm(Mult(lt, Y - m), log = TRUE)) + sum(log(diagonals(lt)))
+ 
+   S <- as.array(Tcrossprod(solve(lt)))
+   ll2 <- sum(l2 <- sapply(1:N, function(i) mvtnorm:::dmvnorm(x = Y[,i], mean = m[,i], sigma = S[,,i], log = TRUE)))
+   chk(ll1, ll2)
+ 
+   l3 <- ldmvnorm(obs = Y, mean = m, invchol = lt, logLik = FALSE)
+   l4 <- ldmvnorm(obs = Y, mean = m, chol = solve(lt), logLik = FALSE)
+ 
+   chk(l2, l3)
+   chk(l2, l4)
+ 
+   l3d <- ldmvnorm(obs = Y, invcholmean = d, invchol = lt, logLik = FALSE)
+   l4d <- ldmvnorm(obs = Y, invcholmean = d, chol = solve(lt), logLik = FALSE)
+ 
+   chk(l3, l3d)
+   chk(l4, l4d)
+ 
+   ll1 <- sum(dnorm(Mult(lt[1,], Y - m[,1]), log = TRUE)) + N * sum(log(diagonals(lt[1,])))
+ 
+   S <- as.array(Tcrossprod(solve(lt)))
+   ll2 <- sum(l2 <- sapply(1:N, function(i) mvtnorm:::dmvnorm(x = Y[,i], mean = m[,1], sigma = S[,,1], log = TRUE)))
+   chk(ll1, ll2)
+ 
+   l3 <- ldmvnorm(obs = Y, mean = m[,1], invchol = lt[1,], logLik = FALSE)
+   l4 <- ldmvnorm(obs = Y, mean = m[,1], chol = solve(lt[1,]), logLik = FALSE)
+ 
+   chk(l2, l3)
+   chk(l2, l4)
+ 
+   l3d <- ldmvnorm(obs = Y, invcholmean = d[,1], invchol = lt[1,], logLik = FALSE)
+   l4d <- ldmvnorm(obs = Y, invcholmean = d[,1], chol = solve(lt[1,]), logLik = FALSE)
+ 
+   chk(l3, l3d)
+   chk(l4, l4d)
+ 
+   ### check scores
+   if (require("numDeriv", quietly = TRUE)) {
+ 
+     f <- function(L) {
+       L <- ltMatrices(L, diag = dg, byrow = br)
+       ldmvnorm(obs = Y, mean = m, invchol = L)
+     }
+ 
+     s0 <- grad(f, unclass(lt))
+     s1 <- sldmvnorm(obs = Y, mean = m, invchol = lt)
+ 
+     chk(Lower_tri(ltMatrices(matrix(s0, ncol = N), diag = dg, byrow = br), diag = dg), 
+         Lower_tri(s1$invchol, diag = dg))
+ 
+     f <- function(L) {
+       L <- ltMatrices(L, diag = dg, byrow = br)
+       ldmvnorm(obs = Y, invcholmean = d, invchol = L)
+     }
+ 
+     s0 <- grad(f, unclass(lt))
+     s1 <- sldmvnorm(obs = Y, invcholmean = d, invchol = lt)
+ 
+     chk(Lower_tri(ltMatrices(matrix(s0, ncol = N), diag = dg, byrow = br), diag = dg), 
+         Lower_tri(s1$invchol, diag = dg))
+ 
+     f <- function(L) {
+       L <- ltMatrices(L, diag = dg, byrow = br)
+       ldmvnorm(obs = Y, mean = m, chol = L)
+     }
+ 
+     s0 <- grad(f, unclass(lt))
+     s1 <- sldmvnorm(obs = Y, mean = m, chol = lt)
+ 
+     chk(Lower_tri(ltMatrices(matrix(s0, ncol = N), diag = dg, byrow = br), diag = dg), 
+         Lower_tri(s1$chol, diag = dg))
+ 
+ 
+     f <- function(L) {
+       L <- ltMatrices(L, diag = dg, byrow = br)
+       ldmvnorm(obs = Y, invcholmean = d, chol = L)
+     }
+ 
+     s0 <- grad(f, unclass(lt))
+     s1 <- sldmvnorm(obs = Y, invcholmean = d, chol = lt)
+ 
+     chk(Lower_tri(ltMatrices(matrix(s0, ncol = N), diag = dg, byrow = br), diag = dg), 
+         Lower_tri(s1$chol, diag = dg))
+ 
+     f <- function(x)
+       ldmvnorm(obs = x, mean = m, invchol = lt)
+ 
+     s0 <- grad(f, Y)
+     s1 <- sldmvnorm(obs = Y, mean = m, invchol = lt)
+ 
+     chk(matrix(s0, ncol = N), s1$obs)
+ 
+     f <- function(x)
+       ldmvnorm(obs = x, invcholmean = d, invchol = lt)
+ 
+     s0 <- grad(f, Y)
+     s1 <- sldmvnorm(obs = Y, invcholmean = d, invchol = lt)
+ 
+     chk(matrix(s0, ncol = N), s1$obs)
+ 
+     f <- function(m)
+       ldmvnorm(obs = Y, mean = m, invchol = lt)
+ 
+     s0 <- grad(f, m)
+     s1 <- sldmvnorm(obs = Y, mean = m, invchol = lt)
+ 
+     chk(matrix(s0, ncol = N), -s1$obs)
+ 
+     f <- function(d)
+       ldmvnorm(obs = Y, invcholmean = d, invchol = lt)
+ 
+     s0 <- grad(f, d)
+     s1 <- sldmvnorm(obs = Y, invcholmean = d, invchol = lt)
+ 
+     chk(matrix(s0, ncol = N), s1$invcholmean)
+   }
+ })
> 
> dg <- TRUE
> br <- FALSE
> eval(thischeck)
> 
> dg <- FALSE
> br <- FALSE
> eval(thischeck)
> 
> dg <- FALSE
> br <- TRUE
> eval(thischeck)
> 
> dg <- FALSE
> br <- FALSE
> eval(thischeck)
> 
> 
> proc.time()
   user  system elapsed 
  11.20    0.32   11.51